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AI and the Future of Code Reviews - A Deep Dive into CodeRabbit

· 10 min read
Gur Singh
Vishu Kaur

We are witnessing an inflection point in the software development industry. Developers around the world have been realizing the incredible possibilities that AI can bring. The introduction of GitHub Copilot and ChatGPT have revolutionized software development. They have been the fastest-growing tools in the history of software development.

While on the code generation side, many tools have emerged, the code review process has remained largely unchanged. We continue to use the same tools and processes that were used 10 years ago. The code is still manually reviewed, which is slow, error-prone, and expensive. To address this, we are building CodeRabbit, an AI-powered code reviewer that is part of the code merge and CI/CD process. With CodeRabbit our vision is to speed up the code merge process by an order of magnitude, while also improving the quality of the code that goes beyond what is possible with human reviewers alone and existing linting tools.

Impediments to shipping quality software @Speed

An average developer spends most of their time between writing and reviewing the code. Typically, the development process involves branching off from the main code base, developing a new feature or fixing a bug, and then merging the code back into the mainline. To write software, developers use modern editors such as Visual Studio Code which include sophisticated language servers, static analyzers and linters. These tools are being rapidly augmented by AI-powered extensions such as GitHub Copilot. Local development tools are just one part of the equation.

Relying on local tools alone is not sufficient to prevent quality issues, as they are inconsistent across the developers which makes it hard to enforce standards. To ensure quality, the code is merged collaboratively in the form of pull requests (PR) in platforms such as GitHub/GitLab. As soon as the PR is opened, the CI/CD process kicks in. The code is linted, compiled and tested. Most importantly, the code is reviewed by a peer who checks for the intention of changes, in addition to looking for coding standards, security vulnerabilities, and other issues. The reviews require a broader context to not just understand the changes but also evaluate the impact on the larger codebase. The reviewer approves the PR, and the code is merged into the main codebase. The code review is required not just for the quality of the code but also for meeting compliance and regulatory requirements.

While the ideal code review process sounds smooth and efficient, the reality is often riddled with challenges and inefficiencies. Specifically, the manual review is often the slowest part of the development process. It is not uncommon for a PR to take days or even weeks to get merged. Here's a brief glimpse into the challenges:

Team Slowdown: The waiting period for code reviews and merges affects not just individual developers but the whole team. Project timelines get stretched, leading to delays in launching new features or fixing critical bugs.

Context Switching: Developers often lose context when they switch from coding to waiting for a review. Getting back into the code takes time and mental energy, which hampers productivity.

Rubber-Stamp Reviews: In dysfunctional teams, the code review process can become a mere formality. Reviewers might approve code without thorough inspection, allowing bugs and vulnerabilities to slip through.

Personality Clashes: Sometimes, friction between the developer and the reviewer goes beyond code quality, leading to nitpicking and unnecessary delays. This can create a toxic work environment, affecting the team's morale.

Job Dissatisfaction: Continual delays and inefficiencies in the code merge process can often demoralize developers to the point where they consider switching jobs, affecting the company's retention rates.

In summary, the status-quo is not ideal, and it is evident from the software bugs, security vulnerabilities, and the service outages that frequently plague the software industry.

Merging code 10x faster with CodeRabbit

CodeRabbit is an AI-powered code reviewer that significantly speeds up the code review process while also improving the quality of the code. It works seamlessly within the pull request workflow and collaborates with the developer and the reviewer to ensure code quality. It goes beyond existing linters and static code analysis tools in uncovering issues and suggesting improvements by providing a human-like understanding of the objective of the code.

With CodeRabbit, developers get context aware feedback within minutes, which enables them to make improvements based on best practices and get their code ready to be merged faster. CodeRabbit also helps reviewers by providing them with confidence and speed to approve the code faster. Reviewers can use CodeRabbit's auto-generated walkthrough and suggestions as a starting point for their review. Peer reviewer can have a three-way collaboration including the developer, and CodeRabbit, which can significantly enrich the review experience while saving them time and effort.

CodeRabbit is built on top of the generative AI to provide the following key capabilities:

Summarization: CodeRabbit summarizes the code changes in the PR and provides a high-level overview. This helps the reviewer and product team to quickly understand the changes and the impact on the product.

Incremental Reviews: CodeRabbit thoroughly reviews the code after each commit and provides incremental feedback to the developer. It uncovers issues and suggests improvements by commenting on the code like a human reviewer.

Chat about changes: CodeRabbit provides conversational capability that allows developers and reviewers to ask questions, generate code, and get feedback in the context of changes.

Designing CodeRabbit

The review process is multi-stage and shown in the figure below. CodeRabbit's workflow is triggered when a developer opens a pull request or commits code to an existing pull request. This is followed by various summarization and review stages.

CodeRabbit is not just a simple wrapper that does pass-through to the LLM models. To circumvent context size limits, CodeRabbit uses an innovative, multi-LLM and multi-stage approach to scale reviews for larger change sets. Unlike AI-based code completion tools, code reviews are a much more complex problem. The reviewer context is much broader than the developer context, as the reviewer needs to uncover not just obvious issues but also understand the larger context of the pull request and changes across multiple files. Below is a glimpse into the challenges we faced and the solutions we came up with:

Context window size: The LLM models have limited context windows, for instance,gpt-3.5-turbo has a context window of 4K or 16K tokens and gpt-4 has a context window of 8K tokens. This is often insufficient to pack larger change sets. To circumvent this, we provide various summaries while reviewing changes to each file and by smartly prioritizing context that is packed in each request.

Inputting and outputting structured content: LLMs are particularly bad at understanding and generating structured content and mathematical computation. We had to design new input formats, that are closer to how humans understand changes, instead of using the standard unified diff format. We also had to provide few-shot examples to the LLMs to get the desired results.

Noise: LLMs are terrible at differentiating between noise and signal. For instance, if you ask LLMs for 20 suggestions, you will get them, but only a few of them will be useful. This is particularly true for code reviews. We had to design a multi-stage review process that reinforces the signal and filters out the noise.

Costs: While advanced models like gpt-4 are great in performing complex tasks, they are several orders of magnitude more expensive than models like gpt-3.5-turbo. We had to design a multi-model approach that uses simpler models for summarizations, while complex models are used for tasks such as reviewing code. In addition, simpler models act as a triage filter that identifies the changes that need to be thoroughly reviewed by more complex models.

Inaccuracies: LLMs are not perfect and often return inaccurate results, and they sometimes even ignore instructions and completely fabricate a response. Rather than keep fighting the LLMs we wrote layers of sanity checks to fix or hide the inaccuracies from the user.

Data privacy: The biggest concern from our users is whether their code is being stored and used to train the models. We made sure that all queries to LLMs are ephemeral, and the data is discarded right away from our service. At the same time, it's challenging to provide stateful incremental reviews without storing the data. We had to design a system that stores all state within the pull request itself and not in our service for maximum privacy.


Building on top of LLMs is a new space, and we can draw parallels to how early software such as MS-DOS were built on top of IBM PC and Intel microprocessors. Even with limited memory, killer applications were built. The context size limits of LLMs are similar to the memory limits of early PCs. Innovation is required to build sophisticated applications on top of LLMs that give the impression of a much larger context, just like early 3D games used innovative techniques to run even on modest PCs. It's quite likely that immense value will be captured by the applications while underlying LLMs and AI infrastructure such as vector databases will continue to get commoditized. In addition, the line between the actual model training and the prompting continues to be blurred. Prompts with few-shot examples and fine-tuning can make a big difference in the quality of results and is an area that will differentiate products that build on top of LLMs.

At CodeRabbit, we are at the forefront of this innovation by building an AI-first developer tools company from the ground up. We are approaching this problem from first principles, as the techniques being used bear little resemblance to the existing tools like linters and static code analysis tools. We are witnessing an inflection point as AI has crossed the practicality threshold, despite its limitations. Furthermore, we believe that we can keep innovating around the limitations and bring sophisticated products that push the boundaries of what is possible with AI. We are excited about our roadmap and hope to unlock immense value for our customers in the near future.